NBA dobás térképek vizsgálata

Mihályi Balázs Márk

J8KAR3

Adatkészlet¶

Az adatkészlet 12 NBA szezont (2012-13-tól 2023-24-ig) ölel föl és 3 táblából áll:

  • Az első tábla csapat szintű statisztikákat tartalmaz. Ki milyen hatékonyan dobta a kettő és hárompontosokat, milyen hatékonyan támadtak és védekeztek, és még sok egyéb statisztikát.
  • A második tábla liga szikntű statisztikákat tartalmaz, dobás zónákra lebontva. Az NBA két féle dobás zóna kategorizálást alkalmaz, egy "alapot" és egy részletesebbet. Ezek leírása/bemutatása később történik meg.
  • A harmadik táblában dobásokra vonatkozó adatok vannak. Melyik meccsen, ki-, mikor-, honnan dobta el és bement-e.

Az adatok letöltésére az nba_api nevű python könyvtárat használtam: https://github.com/swar/nba_api/tree/master

A három adat tábla elérhető az alábbi linken (az első tábla itt elérhető verziója magas tárhely igény miatt csak az első vizsgált szezont tartalmazza): https://bmeedu-my.sharepoint.com/:f:/g/personal/balazsmark_mihalyi_edu_bme_hu/EqIDixkjS85Hukzh2l5Lz90BxB_KviqGNE6UtiwTmV3Q9w?e=Pwq9ej

Feladat¶

A feladat a dobás térképek változásának vizsgálata, az ideális (támadó és védő) dobás térkép keresése csapat szintű statisztikák alapján és egyéb nem egyértelmű összefüggések feltárása. (Fő kérdések a dokumentum végén.)

Feltáró adatelemzés:¶

In [1]:
import pandas as pd
pd.set_option('display.max_columns', None)
import matplotlib.pyplot as plt
import seaborn as sns

Csapat statisztikák¶

A tábla 7 csoportra osztva tartalmaz statisztikákat, 100 labdabirtoklásra átlagolva. Mindegyikhez tartozik egy numerikus, és egy "_RANK" végződésű oszlop. Előbbi a statisztika értékét tartalmazza, utóbbi pedig azt mutatja meg, hogy ez hanyadik helyezésre elég a ligán belül. A "_RANK" tipusú oszlopok értéke ezért 1-től 30-ig terjed, minden szezonban minden értéket pontosan egyzser vesznek fel. Ezen okból kifolyólag a feltáró adatelemzés során külön nem fogom ezeket vizsgálni. A hét csoport a következő:

  • Base

    Általános boxscore statisztikák. PL.: Elvállalt mezőny dobások, bedobott mezőnydobások, stb.

  • Advanced

    Fejlett statisztikák. Pl.: Támadó/Védő értékelés (Offensive/Defensive Rating), stb. Ezek a 100 labdabirtoklásra vetített dobott/kapott pontok számát jelölik.

  • Misc

    Egyéb statisztikák. Pl.: Szerzett labda után szerzett pontok, második esélyből (támadó lepattanó után) szerzett pontok, stb.

  • Four Factors

    A négy faktor:

    • Effektív mezőnyszázelék (súlyozott dobás pontosság),
    • Büntető ráta (a dobások hány százaléka büntető),
    • Eladott labda százalék (a támadások hány százaléka végződött eladott labdával)
    • Támadó lepattanó százalék (A kihagyott dobások hány százalékát szedi vissza a csapat)

  • Scoring

    Pontszerzéssel kapcsolatos statisztikák. Pl.: Kétpontos és hárompontos kísérletek százaléka (mezőnydobásokhoz viszonyítva)

  • Opponent

    Az ellenfelek 'Base' statisztikái ( = az engedett 'Base' statisztikai mutatók)

  • Defense

    Védő statisztikák. Pl.: Védő értékelés (Defensive Rating), védő lepattanók, stb.

Ahogy az sejthető, a 7 csoport redundánsan tartalmaz néhány statisztikát. Ezeket azért nem törlöm, mert a későbbi dashboardon csoportonként akarom megjeleníteni a statisztikákat.

In [2]:
# For simpler plotting, i will load a félattened, merged version of the data
team_stats_simple = pd.read_csv('./data/team_stats_2012_to_2023.csv')
team_stats_simple.head()
Out[2]:
TEAM_ID TEAM_NAME GP W L W_PCT MIN FGM FGA FG_PCT FG3M FG3A FG3_PCT FTM FTA FT_PCT OREB DREB REB AST TOV STL BLK BLKA PF PFD PTS PLUS_MINUS GP_RANK W_RANK L_RANK W_PCT_RANK MIN_RANK FGM_RANK FGA_RANK FG_PCT_RANK FG3M_RANK FG3A_RANK FG3_PCT_RANK FTM_RANK FTA_RANK FT_PCT_RANK OREB_RANK DREB_RANK REB_RANK AST_RANK TOV_RANK STL_RANK BLK_RANK BLKA_RANK PF_RANK PFD_RANK PTS_RANK PLUS_MINUS_RANK E_OFF_RATING OFF_RATING E_DEF_RATING DEF_RATING E_NET_RATING NET_RATING AST_PCT AST_TO AST_RATIO OREB_PCT DREB_PCT REB_PCT TM_TOV_PCT EFG_PCT TS_PCT E_PACE PACE PACE_PER40 POSS PIE OFF_RATING_RANK DEF_RATING_RANK NET_RATING_RANK AST_PCT_RANK AST_TO_RANK AST_RATIO_RANK OREB_PCT_RANK DREB_PCT_RANK REB_PCT_RANK TM_TOV_PCT_RANK EFG_PCT_RANK TS_PCT_RANK PACE_RANK PIE_RANK PTS_OFF_TOV PTS_2ND_CHANCE PTS_FB PTS_PAINT OPP_PTS_OFF_TOV OPP_PTS_2ND_CHANCE OPP_PTS_FB OPP_PTS_PAINT PTS_OFF_TOV_RANK PTS_2ND_CHANCE_RANK PTS_FB_RANK PTS_PAINT_RANK OPP_PTS_OFF_TOV_RANK OPP_PTS_2ND_CHANCE_RANK OPP_PTS_FB_RANK OPP_PTS_PAINT_RANK FTA_RATE OPP_EFG_PCT OPP_FTA_RATE OPP_TOV_PCT OPP_OREB_PCT FTA_RATE_RANK OPP_EFG_PCT_RANK OPP_FTA_RATE_RANK OPP_TOV_PCT_RANK OPP_OREB_PCT_RANK PCT_FGA_2PT PCT_FGA_3PT PCT_PTS_2PT PCT_PTS_2PT_MR PCT_PTS_3PT PCT_PTS_FB PCT_PTS_FT PCT_PTS_OFF_TOV PCT_PTS_PAINT PCT_AST_2PM PCT_UAST_2PM PCT_AST_3PM PCT_UAST_3PM PCT_AST_FGM PCT_UAST_FGM PCT_FGA_2PT_RANK PCT_FGA_3PT_RANK PCT_PTS_2PT_RANK PCT_PTS_2PT_MR_RANK PCT_PTS_3PT_RANK PCT_PTS_FB_RANK PCT_PTS_FT_RANK PCT_PTS_OFF_TOV_RANK PCT_PTS_PAINT_RANK PCT_AST_2PM_RANK PCT_UAST_2PM_RANK PCT_AST_3PM_RANK PCT_UAST_3PM_RANK PCT_AST_FGM_RANK PCT_UAST_FGM_RANK OPP_FGM OPP_FGA OPP_FG_PCT OPP_FG3M OPP_FG3A OPP_FG3_PCT OPP_FTM OPP_FTA OPP_FT_PCT OPP_OREB OPP_DREB OPP_REB OPP_AST OPP_TOV OPP_STL OPP_BLK OPP_BLKA OPP_PF OPP_PFD OPP_PTS OPP_FGM_RANK OPP_FGA_RANK OPP_FG_PCT_RANK OPP_FG3M_RANK OPP_FG3A_RANK OPP_FG3_PCT_RANK OPP_FTM_RANK OPP_FTA_RANK OPP_FT_PCT_RANK OPP_OREB_RANK OPP_DREB_RANK OPP_REB_RANK OPP_AST_RANK OPP_TOV_RANK OPP_STL_RANK OPP_BLK_RANK OPP_BLKA_RANK OPP_PF_RANK OPP_PFD_RANK OPP_PTS_RANK SEASON
0 1610612737 Atlanta Hawks 82 44 38 0.537 51.3 39.8 85.8 0.464 9.1 24.5 0.371 15.0 20.9 0.715 9.8 33.5 43.3 25.9 15.7 8.6 4.8 4.5 19.0 19.9 103.7 0.4 1 14 14 14 19 13 24 7 5 5 7 28 27 26 26 10 26 3 19 13 23 6 3 23 17 13 102.7 103.7 101.8 103.4 0.9 0.3 0.651 1.65 18.8 0.254 0.701 0.482 0.157 0.517 0.546 94.7 93.56 77.97 7744 0.509 17 10 14 1 5 2 28 13 29 19 6 9 12 13 18.1 11.3 18.7 43.6 17.7 14.0 12.7 43.4 14 26 3 17 16 12 3 13 0.244 0.496 0.237 0.161 0.299 25 16 3 10 13 0.714 0.286 0.592 0.172 0.264 0.181 0.144 0.174 0.421 0.585 0.415 0.874 0.126 0.651 0.349 27 4 23 22 3 2 28 11 17 3 28 8 23 1 30 39.7 88.1 0.450 8.0 21.1 0.379 15.9 20.9 0.763 12.0 34.3 46.3 23.6 16.1 8.9 4.5 4.8 19.9 19.0 103.3 16 21 13 19 14 28 4 3 26 17 24 25 13 11 28 6 23 8 28 10 2012-13
1 1610612738 Boston Celtics 81 41 40 0.506 51.9 39.2 84.4 0.465 6.5 18.2 0.358 17.2 22.2 0.776 8.5 33.1 41.6 24.1 15.4 8.7 4.8 4.9 22.4 20.6 102.2 -0.2 29 16 16 16 15 23 28 6 25 27 15 21 20 6 29 12 30 10 11 9 22 10 24 20 24 16 101.1 102.2 100.4 102.6 0.7 -0.5 0.614 1.56 17.9 0.246 0.690 0.477 0.154 0.503 0.542 94.0 92.46 77.05 7652 0.507 24 7 16 6 13 5 29 23 30 11 12 12 17 14 17.6 10.5 14.1 40.3 17.9 15.1 12.8 45.2 16 29 15 26 18 27 6 17 0.263 0.482 0.292 0.162 0.310 16 6 25 9 23 0.785 0.215 0.640 0.246 0.191 0.138 0.169 0.173 0.394 0.555 0.445 0.914 0.086 0.614 0.386 7 24 7 2 23 14 16 13 26 9 22 2 29 6 25 38.2 86.8 0.441 7.2 21.0 0.342 18.7 25.3 0.740 12.1 34.0 46.1 22.9 16.2 8.3 4.9 4.8 20.6 22.4 102.4 5 8 7 8 13 4 24 24 8 20 22 24 10 9 14 10 22 11 7 7 2012-13
2 1610612751 Brooklyn Nets 82 49 33 0.598 53.4 39.6 88.0 0.450 8.4 23.7 0.357 19.3 26.3 0.731 14.1 33.1 47.2 22.4 16.2 8.1 5.3 5.2 20.2 23.6 106.9 2.0 1 8 9 9 3 15 12 13 7 7 17 6 6 24 2 13 4 25 26 18 17 16 8 4 9 10 105.0 106.9 103.6 104.8 1.4 2.0 0.567 1.38 16.1 0.343 0.701 0.520 0.162 0.498 0.536 91.2 89.88 74.90 7433 0.509 9 16 10 25 28 25 3 14 3 26 14 13 28 12 17.5 15.6 10.0 45.7 18.7 13.7 15.3 47.9 17 8 29 11 26 10 25 26 0.299 0.503 0.238 0.148 0.299 7 20 4 24 14 0.731 0.269 0.583 0.155 0.237 0.093 0.180 0.164 0.427 0.484 0.516 0.871 0.129 0.567 0.433 24 7 26 25 8 29 8 19 13 29 2 9 22 25 6 41.2 89.0 0.464 7.0 19.0 0.366 15.4 21.1 0.731 11.8 31.4 43.2 23.6 14.8 8.3 5.2 5.3 23.6 20.2 104.9 25 25 23 7 5 21 2 4 1 15 5 5 14 24 12 16 17 27 23 16 2012-13
3 1610612766 Charlotte Bobcats 82 21 61 0.256 51.9 37.0 87.1 0.425 6.1 18.3 0.335 20.3 27.0 0.750 12.0 31.3 43.3 20.8 15.1 7.7 6.3 7.4 20.5 22.9 100.4 -9.9 1 29 29 29 14 30 19 30 27 26 27 2 3 18 16 28 25 30 8 24 6 30 10 5 28 30 98.3 100.4 108.9 110.3 -10.6 -9.9 0.562 1.38 15.3 0.305 0.679 0.486 0.151 0.460 0.507 94.0 92.49 77.07 7632 0.438 28 30 30 27 29 30 16 26 25 8 30 30 16 30 15.5 14.4 16.9 41.5 18.2 15.0 14.9 47.2 28 18 7 25 23 26 23 24 0.310 0.524 0.264 0.151 0.321 3 30 12 21 26 0.790 0.210 0.615 0.201 0.184 0.168 0.202 0.155 0.413 0.507 0.493 0.838 0.162 0.562 0.438 6 25 16 13 26 4 2 27 19 26 5 20 11 27 4 41.7 88.6 0.471 9.4 24.2 0.388 17.5 23.4 0.747 12.7 34.7 47.4 27.1 15.1 7.5 7.4 6.3 22.9 20.5 110.3 29 24 27 30 30 29 9 14 9 28 25 28 30 21 2 30 6 26 21 29 2012-13
4 1610612741 Chicago Bulls 82 45 37 0.549 53.2 39.2 89.8 0.437 6.0 17.0 0.353 18.0 23.3 0.773 13.8 33.7 47.5 25.3 15.7 7.9 5.6 6.4 21.7 21.6 102.4 0.3 1 11 11 11 4 22 6 25 28 29 21 14 17 9 4 9 2 4 15 20 13 25 18 13 23 14 100.4 102.4 100.3 101.9 0.1 0.5 0.645 1.61 17.8 0.332 0.699 0.514 0.157 0.470 0.512 92.0 90.35 75.29 7460 0.514 23 6 13 2 8 7 6 16 5 15 29 26 27 8 16.5 16.4 11.2 43.8 16.9 13.9 14.4 43.2 24 2 22 16 5 11 16 11 0.259 0.477 0.278 0.150 0.301 19 4 19 22 16 0.811 0.189 0.649 0.222 0.175 0.109 0.176 0.161 0.427 0.610 0.390 0.839 0.161 0.645 0.355 2 29 5 5 28 22 11 22 14 1 30 19 12 2 29 39.0 88.0 0.443 6.1 17.5 0.346 18.1 24.5 0.739 12.1 33.1 45.2 20.7 15.1 8.0 6.4 5.6 21.6 21.7 102.1 10 19 9 2 1 5 19 22 7 19 14 17 1 22 8 25 13 18 13 6 2012-13
In [3]:
team_stats_simple.describe()
Out[3]:
TEAM_ID GP W L W_PCT MIN FGM FGA FG_PCT FG3M FG3A FG3_PCT FTM FTA FT_PCT OREB DREB REB AST TOV STL BLK BLKA PF PFD PTS PLUS_MINUS GP_RANK W_RANK L_RANK W_PCT_RANK MIN_RANK FGM_RANK FGA_RANK FG_PCT_RANK FG3M_RANK FG3A_RANK FG3_PCT_RANK FTM_RANK FTA_RANK FT_PCT_RANK OREB_RANK DREB_RANK REB_RANK AST_RANK TOV_RANK STL_RANK BLK_RANK BLKA_RANK PF_RANK PFD_RANK PTS_RANK PLUS_MINUS_RANK E_OFF_RATING OFF_RATING E_DEF_RATING DEF_RATING E_NET_RATING NET_RATING AST_PCT AST_TO AST_RATIO OREB_PCT DREB_PCT REB_PCT TM_TOV_PCT EFG_PCT TS_PCT E_PACE PACE PACE_PER40 POSS PIE OFF_RATING_RANK DEF_RATING_RANK NET_RATING_RANK AST_PCT_RANK AST_TO_RANK AST_RATIO_RANK OREB_PCT_RANK DREB_PCT_RANK REB_PCT_RANK TM_TOV_PCT_RANK EFG_PCT_RANK TS_PCT_RANK PACE_RANK PIE_RANK PTS_OFF_TOV PTS_2ND_CHANCE PTS_FB PTS_PAINT OPP_PTS_OFF_TOV OPP_PTS_2ND_CHANCE OPP_PTS_FB OPP_PTS_PAINT PTS_OFF_TOV_RANK PTS_2ND_CHANCE_RANK PTS_FB_RANK PTS_PAINT_RANK OPP_PTS_OFF_TOV_RANK OPP_PTS_2ND_CHANCE_RANK OPP_PTS_FB_RANK OPP_PTS_PAINT_RANK FTA_RATE OPP_EFG_PCT OPP_FTA_RATE OPP_TOV_PCT OPP_OREB_PCT FTA_RATE_RANK OPP_EFG_PCT_RANK OPP_FTA_RATE_RANK OPP_TOV_PCT_RANK OPP_OREB_PCT_RANK PCT_FGA_2PT PCT_FGA_3PT PCT_PTS_2PT PCT_PTS_2PT_MR PCT_PTS_3PT PCT_PTS_FB PCT_PTS_FT PCT_PTS_OFF_TOV PCT_PTS_PAINT PCT_AST_2PM PCT_UAST_2PM PCT_AST_3PM PCT_UAST_3PM PCT_AST_FGM PCT_UAST_FGM PCT_FGA_2PT_RANK PCT_FGA_3PT_RANK PCT_PTS_2PT_RANK PCT_PTS_2PT_MR_RANK PCT_PTS_3PT_RANK PCT_PTS_FB_RANK PCT_PTS_FT_RANK PCT_PTS_OFF_TOV_RANK PCT_PTS_PAINT_RANK PCT_AST_2PM_RANK PCT_UAST_2PM_RANK PCT_AST_3PM_RANK PCT_UAST_3PM_RANK PCT_AST_FGM_RANK PCT_UAST_FGM_RANK OPP_FGM OPP_FGA OPP_FG_PCT OPP_FG3M OPP_FG3A OPP_FG3_PCT OPP_FTM OPP_FTA OPP_FT_PCT OPP_OREB OPP_DREB OPP_REB OPP_AST OPP_TOV OPP_STL OPP_BLK OPP_BLKA OPP_PF OPP_PFD OPP_PTS OPP_FGM_RANK OPP_FGA_RANK OPP_FG_PCT_RANK OPP_FG3M_RANK OPP_FG3A_RANK OPP_FG3_PCT_RANK OPP_FTM_RANK OPP_FTA_RANK OPP_FT_PCT_RANK OPP_OREB_RANK OPP_DREB_RANK OPP_REB_RANK OPP_AST_RANK OPP_TOV_RANK OPP_STL_RANK OPP_BLK_RANK OPP_BLKA_RANK OPP_PF_RANK OPP_PFD_RANK OPP_PTS_RANK
count 3.600000e+02 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.00000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.00000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000 360.000000
mean 1.610613e+09 80.211111 40.105556 40.105556 0.499572 49.161111 40.386667 87.754167 0.460314 10.562778 29.449444 0.357981 17.674167 23.034444 0.768108 10.585833 33.853889 44.440278 24.090000 14.413333 7.774444 4.946667 4.949722 20.346944 20.348056 109.013611 -0.005278 2.138889 15.147222 15.136111 15.172222 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 107.177778 109.013611 107.18750 109.020833 -0.009722 -0.007778 0.596033 1.684306 17.504722 0.279269 0.720358 0.499950 0.144133 0.520467 0.556914 99.416389 97.740861 81.450806 7889.688889 0.499797 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.494444 15.500000 16.738611 13.346389 13.414722 46.483333 16.736667 13.335000 13.440278 46.497500 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 0.262853 0.520517 0.262889 0.144133 0.279642 15.500000 15.500000 15.500000 15.497222 15.500000 0.664556 0.335444 0.548531 0.122056 0.289136 0.123122 0.162336 0.153800 0.426433 0.512461 0.487539 0.835019 0.164981 0.596033 0.403967 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 40.390556 87.742778 0.460350 10.57000 29.452500 0.358303 17.673889 23.033333 0.767689 10.577778 33.857778 44.436111 24.093889 14.413611 7.773056 4.949722 4.946667 20.348333 20.346944 109.019167 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.497222 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000
std 8.667488e+00 4.111928 12.149256 11.984901 0.148145 1.604811 1.568986 1.840245 0.016649 2.401521 6.285501 0.016917 1.669912 2.217550 0.030395 1.337417 1.488353 1.860396 2.035195 1.158757 0.839925 0.770443 0.731846 1.513557 1.418774 4.598802 4.773966 4.666318 8.740964 8.737638 8.740665 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 4.578484 4.598802 4.22285 4.258694 4.936938 4.732604 0.038721 0.214514 1.275505 0.025053 0.018862 0.013651 0.011588 0.025332 0.023744 3.261514 3.164719 2.636805 403.597441 0.029789 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.664915 8.667488 1.541201 1.476780 2.580402 4.026140 1.387625 1.024082 1.594756 3.432569 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 0.028368 0.022922 0.027138 0.012779 0.018862 8.667488 8.667488 8.667488 8.663630 8.667488 0.070765 0.070765 0.051505 0.052610 0.057667 0.023501 0.016102 0.015423 0.033050 0.044749 0.044749 0.047434 0.047434 0.038721 0.038721 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 1.529403 1.697697 0.015017 2.06624 5.488082 0.013057 1.544074 2.070283 0.016132 0.930993 1.449616 1.561560 1.734852 1.280172 0.690219 0.731846 0.770443 1.418461 1.513557 4.275817 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.671664 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488 8.667488
min 1.610613e+09 64.000000 10.000000 9.000000 0.122000 45.500000 34.800000 82.100000 0.408000 5.200000 15.000000 0.305000 12.800000 17.400000 0.668000 7.700000 30.500000 39.400000 18.600000 11.200000 5.700000 2.400000 3.100000 15.300000 17.400000 94.900000 -10.400000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 93.000000 94.900000 96.60000 98.200000 -10.700000 -10.600000 0.472000 1.160000 14.100000 0.216000 0.673000 0.459000 0.112000 0.456000 0.494000 90.900000 89.500000 74.590000 6317.000000 0.424000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 12.400000 10.200000 8.300000 35.100000 13.200000 10.500000 9.500000 36.500000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.194000 0.453000 0.189000 0.115000 0.225000 1.000000 1.000000 1.000000 1.000000 1.000000 0.481000 0.166000 0.406000 0.027000 0.150000 0.076000 0.124000 0.117000 0.335000 0.372000 0.344000 0.626000 0.046000 0.472000 0.288000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 36.700000 81.700000 0.414000 5.90000 17.500000 0.322000 13.500000 17.600000 0.727000 8.300000 29.400000 40.700000 19.300000 11.500000 6.100000 3.100000 2.400000 17.400000 15.300000 98.100000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
25% 1.610613e+09 82.000000 31.000000 31.000000 0.390000 48.000000 39.300000 86.500000 0.448000 8.800000 24.900000 0.347000 16.500000 21.575000 0.749750 9.600000 32.700000 43.275000 22.700000 13.700000 7.200000 4.400000 4.500000 19.300000 19.400000 105.300000 -3.100000 1.000000 8.000000 7.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 103.400000 105.300000 104.20000 106.075000 -3.325000 -3.150000 0.572000 1.530000 16.600000 0.261000 0.708750 0.491000 0.137000 0.501750 0.540000 97.400000 95.687500 79.740000 7678.500000 0.481000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 15.700000 12.400000 11.500000 43.800000 15.700000 12.700000 12.400000 44.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 0.243000 0.505000 0.244750 0.135000 0.267000 8.000000 8.000000 8.000000 8.000000 8.000000 0.613750 0.284000 0.512000 0.077000 0.248750 0.105000 0.152000 0.143000 0.403000 0.484000 0.458000 0.809000 0.129750 0.572000 0.379000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 39.300000 86.600000 0.450000 8.77500 24.300000 0.349000 16.600000 21.600000 0.757000 9.900000 32.900000 43.300000 23.000000 13.500000 7.300000 4.500000 4.400000 19.400000 19.300000 106.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000 8.000000
50% 1.610613e+09 82.000000 42.000000 39.000000 0.512000 49.000000 40.300000 87.700000 0.460500 10.800000 30.050000 0.357000 17.550000 22.800000 0.771000 10.500000 33.900000 44.400000 24.000000 14.300000 7.700000 4.900000 4.900000 20.400000 20.200000 109.300000 0.400000 1.000000 15.000000 15.000000 15.000000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 107.400000 109.300000 107.25000 109.000000 0.500000 0.350000 0.595500 1.670000 17.400000 0.278000 0.721000 0.500000 0.143000 0.521500 0.556500 99.700000 97.935000 81.615000 7985.000000 0.503000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.000000 15.500000 16.700000 13.300000 13.100000 46.250000 16.700000 13.400000 13.400000 46.300000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 0.259500 0.521000 0.261000 0.144000 0.279000 15.500000 15.500000 15.500000 15.500000 15.500000 0.655500 0.344500 0.546000 0.114000 0.293000 0.119500 0.161000 0.153500 0.427000 0.510000 0.490000 0.838000 0.162000 0.595500 0.404500 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 40.400000 87.700000 0.460000 10.95000 30.300000 0.358000 17.600000 22.950000 0.768000 10.500000 33.800000 44.400000 24.100000 14.400000 7.700000 4.900000 4.900000 20.200000 20.400000 109.000000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000 15.500000
75% 1.610613e+09 82.000000 49.000000 49.000000 0.610000 50.100000 41.600000 89.000000 0.471000 12.300000 33.900000 0.370000 18.700000 24.500000 0.788000 11.400000 35.000000 45.700000 25.400000 15.200000 8.325000 5.400000 5.400000 21.325000 21.200000 112.500000 3.300000 1.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 110.525000 112.500000 110.30000 112.000000 3.200000 3.225000 0.621000 1.820000 18.300000 0.294000 0.733000 0.510000 0.152000 0.538000 0.574000 101.725000 99.910000 83.260000 8160.250000 0.519000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 17.800000 14.300000 15.000000 49.000000 17.700000 14.000000 14.500000 48.800000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 0.280000 0.539000 0.281000 0.153000 0.291250 23.000000 23.000000 23.000000 23.000000 23.000000 0.716000 0.386250 0.583250 0.167000 0.330500 0.137250 0.173000 0.164000 0.447000 0.542000 0.516000 0.870250 0.191000 0.621000 0.428000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 41.400000 88.900000 0.470000 12.20000 33.725000 0.367000 18.700000 24.400000 0.779250 11.200000 34.800000 45.400000 25.300000 15.300000 8.300000 5.400000 5.400000 21.200000 21.325000 112.025000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000 23.000000
max 1.610613e+09 82.000000 73.000000 72.000000 0.890000 53.600000 46.000000 93.600000 0.507000 16.800000 45.800000 0.416000 23.300000 31.700000 0.839000 15.200000 39.800000 50.100000 30.200000 18.300000 10.400000 8.000000 7.400000 24.500000 24.800000 122.200000 11.500000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 120.200000 122.200000 117.70000 119.600000 12.100000 11.700000 0.712000 2.380000 21.200000 0.347000 0.775000 0.541000 0.183000 0.578000 0.610000 107.200000 105.510000 87.930000 8642.000000 0.579000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 21.400000 18.500000 22.400000 59.800000 21.200000 16.000000 18.300000 57.100000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 0.386000 0.576000 0.341000 0.181000 0.327000 30.000000 30.000000 30.000000 30.000000 30.000000 0.834000 0.519000 0.689000 0.253000 0.431000 0.195000 0.220000 0.198000 0.547000 0.656000 0.628000 0.954000 0.374000 0.712000 0.528000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 44.700000 92.900000 0.507000 14.80000 40.200000 0.395000 22.500000 28.700000 0.807000 13.100000 38.100000 49.100000 29.500000 18.100000 9.800000 7.400000 8.000000 24.800000 24.500000 119.400000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000 30.000000
In [4]:
team_stats_all = pd.read_pickle('./data/team_stats_multiindex_2012_to_2023.pkl')
team_stats_all.head()
Out[4]:
Measure TEAM_ID TEAM_NAME GP W L W_PCT Base Advanced Misc Four Factors Scoring Opponent Defense SEASON
Stat FGM FGA FG_PCT FG3M FG3A FG3_PCT FTM FTA FT_PCT OREB DREB REB AST TOV STL BLK BLKA PF PFD PTS PLUS_MINUS FGM_RANK FGA_RANK FG_PCT_RANK FG3M_RANK FG3A_RANK FG3_PCT_RANK FTM_RANK FTA_RANK FT_PCT_RANK OREB_RANK DREB_RANK REB_RANK AST_RANK TOV_RANK STL_RANK BLK_RANK BLKA_RANK PF_RANK PFD_RANK PTS_RANK PLUS_MINUS_RANK E_OFF_RATING OFF_RATING E_DEF_RATING DEF_RATING E_NET_RATING NET_RATING AST_PCT AST_TO AST_RATIO OREB_PCT DREB_PCT REB_PCT TM_TOV_PCT EFG_PCT TS_PCT E_PACE PACE PACE_PER40 POSS PIE OFF_RATING_RANK DEF_RATING_RANK NET_RATING_RANK AST_PCT_RANK AST_TO_RANK AST_RATIO_RANK OREB_PCT_RANK DREB_PCT_RANK REB_PCT_RANK TM_TOV_PCT_RANK EFG_PCT_RANK TS_PCT_RANK PACE_RANK PIE_RANK PTS_OFF_TOV PTS_2ND_CHANCE PTS_FB PTS_PAINT OPP_PTS_OFF_TOV OPP_PTS_2ND_CHANCE OPP_PTS_FB OPP_PTS_PAINT PTS_OFF_TOV_RANK PTS_2ND_CHANCE_RANK PTS_FB_RANK PTS_PAINT_RANK OPP_PTS_OFF_TOV_RANK OPP_PTS_2ND_CHANCE_RANK OPP_PTS_FB_RANK OPP_PTS_PAINT_RANK EFG_PCT FTA_RATE TM_TOV_PCT OREB_PCT OPP_EFG_PCT OPP_FTA_RATE OPP_TOV_PCT OPP_OREB_PCT EFG_PCT_RANK FTA_RATE_RANK TM_TOV_PCT_RANK OREB_PCT_RANK OPP_EFG_PCT_RANK OPP_FTA_RATE_RANK OPP_TOV_PCT_RANK OPP_OREB_PCT_RANK PCT_FGA_2PT PCT_FGA_3PT PCT_PTS_2PT PCT_PTS_2PT_MR PCT_PTS_3PT PCT_PTS_FB PCT_PTS_FT PCT_PTS_OFF_TOV PCT_PTS_PAINT PCT_AST_2PM PCT_UAST_2PM PCT_AST_3PM PCT_UAST_3PM PCT_AST_FGM PCT_UAST_FGM PCT_FGA_2PT_RANK PCT_FGA_3PT_RANK PCT_PTS_2PT_RANK PCT_PTS_2PT_MR_RANK PCT_PTS_3PT_RANK PCT_PTS_FB_RANK PCT_PTS_FT_RANK PCT_PTS_OFF_TOV_RANK PCT_PTS_PAINT_RANK PCT_AST_2PM_RANK PCT_UAST_2PM_RANK PCT_AST_3PM_RANK PCT_UAST_3PM_RANK PCT_AST_FGM_RANK PCT_UAST_FGM_RANK OPP_FGM OPP_FGA OPP_FG_PCT OPP_FG3M OPP_FG3A OPP_FG3_PCT OPP_FTM OPP_FTA OPP_FT_PCT OPP_OREB OPP_DREB OPP_REB OPP_AST OPP_TOV OPP_STL OPP_BLK OPP_BLKA OPP_PF OPP_PFD OPP_PTS PLUS_MINUS OPP_FGM_RANK OPP_FGA_RANK OPP_FG_PCT_RANK OPP_FG3M_RANK OPP_FG3A_RANK OPP_FG3_PCT_RANK OPP_FTM_RANK OPP_FTA_RANK OPP_FT_PCT_RANK OPP_OREB_RANK OPP_DREB_RANK OPP_REB_RANK OPP_AST_RANK OPP_TOV_RANK OPP_STL_RANK OPP_BLK_RANK OPP_BLKA_RANK OPP_PF_RANK OPP_PFD_RANK OPP_PTS_RANK PLUS_MINUS_RANK DEF_RATING DREB DREB_PCT STL BLK OPP_PTS_OFF_TOV OPP_PTS_2ND_CHANCE OPP_PTS_FB OPP_PTS_PAINT DEF_RATING_RANK DREB_RANK DREB_PCT_RANK STL_RANK BLK_RANK OPP_PTS_OFF_TOV_RANK OPP_PTS_2ND_CHANCE_RANK OPP_PTS_FB_RANK OPP_PTS_PAINT_RANK
0 1610612737 Atlanta Hawks 82 44 38 0.537 39.8 85.8 0.464 9.1 24.5 0.371 15.0 20.9 0.715 9.8 33.5 43.3 25.9 15.7 8.6 4.8 4.5 19.0 19.9 103.7 0.4 13 24 7 5 5 7 28 27 26 26 10 26 3 19 13 23 6 3 23 17 13 102.7 103.7 101.8 103.4 0.9 0.3 0.651 1.65 18.8 0.254 0.701 0.482 0.157 0.517 0.546 94.7 93.56 77.97 7744 0.509 17 10 14 1 5 2 28 13 29 19 6 9 12 13 18.1 11.3 18.7 43.6 17.7 14.0 12.7 43.4 14 26 3 17 16 12 3 13 0.517 0.244 0.157 0.254 0.496 0.237 0.161 0.299 6 25 19 28 16 3 10 13 0.714 0.286 0.592 0.172 0.264 0.181 0.144 0.174 0.421 0.585 0.415 0.874 0.126 0.651 0.349 27 4 23 22 3 2 28 11 17 3 28 8 23 1 30 39.7 88.1 0.450 8.0 21.1 0.379 15.9 20.9 0.763 12.0 34.3 46.3 23.6 16.1 8.9 4.5 4.8 19.9 19.0 103.3 -0.4 16 21 13 19 14 28 4 3 26 17 24 25 13 11 28 6 23 8 28 10 13 103.4 33.5 0.701 8.6 4.8 17.7 14.0 12.7 43.4 10 10 13 13 23 16 12 3 13 2012-13
1 1610612738 Boston Celtics 81 41 40 0.506 39.2 84.4 0.465 6.5 18.2 0.358 17.2 22.2 0.776 8.5 33.1 41.6 24.1 15.4 8.7 4.8 4.9 22.4 20.6 102.2 -0.2 23 28 6 25 27 15 21 20 6 29 12 30 10 11 9 22 10 24 20 24 16 101.1 102.2 100.4 102.6 0.7 -0.5 0.614 1.56 17.9 0.246 0.690 0.477 0.154 0.503 0.542 94.0 92.46 77.05 7652 0.507 24 7 16 6 13 5 29 23 30 11 12 12 17 14 17.6 10.5 14.1 40.3 17.9 15.1 12.8 45.2 16 29 15 26 18 27 6 17 0.503 0.263 0.154 0.246 0.482 0.292 0.162 0.310 12 16 11 29 6 25 9 23 0.785 0.215 0.640 0.246 0.191 0.138 0.169 0.173 0.394 0.555 0.445 0.914 0.086 0.614 0.386 7 24 7 2 23 14 16 13 26 9 22 2 29 6 25 38.2 86.8 0.441 7.2 21.0 0.342 18.7 25.3 0.740 12.1 34.0 46.1 22.9 16.2 8.3 4.9 4.8 20.6 22.4 102.4 0.2 5 8 7 8 13 4 24 24 8 20 22 24 10 9 14 10 22 11 7 7 16 102.6 33.1 0.690 8.7 4.8 17.9 15.1 12.8 45.2 7 12 23 9 22 18 27 6 17 2012-13
2 1610612751 Brooklyn Nets 82 49 33 0.598 39.6 88.0 0.450 8.4 23.7 0.357 19.3 26.3 0.731 14.1 33.1 47.2 22.4 16.2 8.1 5.3 5.2 20.2 23.6 106.9 2.0 15 12 13 7 7 17 6 6 24 2 13 4 25 26 18 17 16 8 4 9 10 105.0 106.9 103.6 104.8 1.4 2.0 0.567 1.38 16.1 0.343 0.701 0.520 0.162 0.498 0.536 91.2 89.88 74.90 7433 0.509 9 16 10 25 28 25 3 14 3 26 14 13 28 12 17.5 15.6 10.0 45.7 18.7 13.7 15.3 47.9 17 8 29 11 26 10 25 26 0.498 0.299 0.162 0.343 0.503 0.238 0.148 0.299 14 7 26 3 20 4 24 14 0.731 0.269 0.583 0.155 0.237 0.093 0.180 0.164 0.427 0.484 0.516 0.871 0.129 0.567 0.433 24 7 26 25 8 29 8 19 13 29 2 9 22 25 6 41.2 89.0 0.464 7.0 19.0 0.366 15.4 21.1 0.731 11.8 31.4 43.2 23.6 14.8 8.3 5.2 5.3 23.6 20.2 104.9 -2.0 25 25 23 7 5 21 2 4 1 15 5 5 14 24 12 16 17 27 23 16 10 104.8 33.1 0.701 8.1 5.3 18.7 13.7 15.3 47.9 16 13 14 18 17 26 10 25 26 2012-13
3 1610612766 Charlotte Bobcats 82 21 61 0.256 37.0 87.1 0.425 6.1 18.3 0.335 20.3 27.0 0.750 12.0 31.3 43.3 20.8 15.1 7.7 6.3 7.4 20.5 22.9 100.4 -9.9 30 19 30 27 26 27 2 3 18 16 28 25 30 8 24 6 30 10 5 28 30 98.3 100.4 108.9 110.3 -10.6 -9.9 0.562 1.38 15.3 0.305 0.679 0.486 0.151 0.460 0.507 94.0 92.49 77.07 7632 0.438 28 30 30 27 29 30 16 26 25 8 30 30 16 30 15.5 14.4 16.9 41.5 18.2 15.0 14.9 47.2 28 18 7 25 23 26 23 24 0.460 0.310 0.151 0.305 0.524 0.264 0.151 0.321 30 3 8 16 30 12 21 26 0.790 0.210 0.615 0.201 0.184 0.168 0.202 0.155 0.413 0.507 0.493 0.838 0.162 0.562 0.438 6 25 16 13 26 4 2 27 19 26 5 20 11 27 4 41.7 88.6 0.471 9.4 24.2 0.388 17.5 23.4 0.747 12.7 34.7 47.4 27.1 15.1 7.5 7.4 6.3 22.9 20.5 110.3 9.9 29 24 27 30 30 29 9 14 9 28 25 28 30 21 2 30 6 26 21 29 30 110.3 31.3 0.679 7.7 6.3 18.2 15.0 14.9 47.2 30 28 26 24 6 23 26 23 24 2012-13
4 1610612741 Chicago Bulls 82 45 37 0.549 39.2 89.8 0.437 6.0 17.0 0.353 18.0 23.3 0.773 13.8 33.7 47.5 25.3 15.7 7.9 5.6 6.4 21.7 21.6 102.4 0.3 22 6 25 28 29 21 14 17 9 4 9 2 4 15 20 13 25 18 13 23 14 100.4 102.4 100.3 101.9 0.1 0.5 0.645 1.61 17.8 0.332 0.699 0.514 0.157 0.470 0.512 92.0 90.35 75.29 7460 0.514 23 6 13 2 8 7 6 16 5 15 29 26 27 8 16.5 16.4 11.2 43.8 16.9 13.9 14.4 43.2 24 2 22 16 5 11 16 11 0.470 0.259 0.157 0.332 0.477 0.278 0.150 0.301 29 19 15 6 4 19 22 16 0.811 0.189 0.649 0.222 0.175 0.109 0.176 0.161 0.427 0.610 0.390 0.839 0.161 0.645 0.355 2 29 5 5 28 22 11 22 14 1 30 19 12 2 29 39.0 88.0 0.443 6.1 17.5 0.346 18.1 24.5 0.739 12.1 33.1 45.2 20.7 15.1 8.0 6.4 5.6 21.6 21.7 102.1 -0.3 10 19 9 2 1 5 19 22 7 19 14 17 1 22 8 25 13 18 13 6 14 101.9 33.7 0.699 7.9 5.6 16.9 13.9 14.4 43.2 6 9 16 20 13 5 11 16 11 2012-13
In [5]:
team_stats_all[["TEAM_ID","Base"]]
Out[5]:
Measure TEAM_ID Base
Stat FGM FGA FG_PCT FG3M FG3A FG3_PCT FTM FTA FT_PCT OREB DREB REB AST TOV STL BLK BLKA PF PFD PTS PLUS_MINUS FGM_RANK FGA_RANK FG_PCT_RANK FG3M_RANK FG3A_RANK FG3_PCT_RANK FTM_RANK FTA_RANK FT_PCT_RANK OREB_RANK DREB_RANK REB_RANK AST_RANK TOV_RANK STL_RANK BLK_RANK BLKA_RANK PF_RANK PFD_RANK PTS_RANK PLUS_MINUS_RANK
0 1610612737 39.8 85.8 0.464 9.1 24.5 0.371 15.0 20.9 0.715 9.8 33.5 43.3 25.9 15.7 8.6 4.8 4.5 19.0 19.9 103.7 0.4 13 24 7 5 5 7 28 27 26 26 10 26 3 19 13 23 6 3 23 17 13
1 1610612738 39.2 84.4 0.465 6.5 18.2 0.358 17.2 22.2 0.776 8.5 33.1 41.6 24.1 15.4 8.7 4.8 4.9 22.4 20.6 102.2 -0.2 23 28 6 25 27 15 21 20 6 29 12 30 10 11 9 22 10 24 20 24 16
2 1610612751 39.6 88.0 0.450 8.4 23.7 0.357 19.3 26.3 0.731 14.1 33.1 47.2 22.4 16.2 8.1 5.3 5.2 20.2 23.6 106.9 2.0 15 12 13 7 7 17 6 6 24 2 13 4 25 26 18 17 16 8 4 9 10
3 1610612766 37.0 87.1 0.425 6.1 18.3 0.335 20.3 27.0 0.750 12.0 31.3 43.3 20.8 15.1 7.7 6.3 7.4 20.5 22.9 100.4 -9.9 30 19 30 27 26 27 2 3 18 16 28 25 30 8 24 6 30 10 5 28 30
4 1610612741 39.2 89.8 0.437 6.0 17.0 0.353 18.0 23.3 0.773 13.8 33.7 47.5 25.3 15.7 7.9 5.6 6.4 21.7 21.6 102.4 0.3 22 6 25 28 29 21 14 17 9 4 9 2 4 15 20 13 25 18 13 23 14
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
25 1610612758 43.2 90.6 0.477 14.3 39.1 0.366 15.5 20.8 0.745 10.8 33.1 43.8 28.3 13.1 7.6 4.2 4.5 19.8 18.3 116.2 1.7 7 9 14 4 3 16 27 21 30 14 16 14 7 11 14 29 4 24 21 13 17
26 1610612759 40.9 88.4 0.462 12.3 35.5 0.347 15.2 19.5 0.782 10.1 33.0 43.1 29.1 14.8 6.9 6.2 4.5 16.8 17.5 109.3 -6.3 26 22 25 20 13 28 28 29 14 20 17 21 3 24 23 3 5 3 29 26 24
27 1610612761 42.1 89.3 0.471 11.4 32.9 0.347 16.2 21.5 0.756 10.8 31.6 42.4 28.3 13.9 7.7 4.7 5.8 18.3 18.0 111.8 -6.4 18 13 16 28 24 27 20 16 29 13 26 25 6 20 13 20 23 10 23 24 25
28 1610612762 41.6 89.0 0.467 12.8 36.1 0.354 18.6 22.4 0.830 12.1 32.9 45.0 27.0 15.5 6.5 5.5 6.4 18.4 19.0 114.5 -4.8 21 14 19 16 11 22 5 13 1 4 19 6 14 30 29 10 28 12 12 18 23
29 1610612764 41.6 88.6 0.470 12.0 34.5 0.348 15.0 19.6 0.764 8.9 30.9 39.8 27.1 13.6 7.4 4.9 5.9 19.3 17.4 110.2 -9.0 20 19 17 23 17 25 29 28 24 28 28 30 12 16 19 17 25 23 30 25 27

360 rows × 43 columns

In [6]:
team_stats_all["Opponent"]
Out[6]:
Stat OPP_FGM OPP_FGA OPP_FG_PCT OPP_FG3M OPP_FG3A OPP_FG3_PCT OPP_FTM OPP_FTA OPP_FT_PCT OPP_OREB OPP_DREB OPP_REB OPP_AST OPP_TOV OPP_STL OPP_BLK OPP_BLKA OPP_PF OPP_PFD OPP_PTS PLUS_MINUS OPP_FGM_RANK OPP_FGA_RANK OPP_FG_PCT_RANK OPP_FG3M_RANK OPP_FG3A_RANK OPP_FG3_PCT_RANK OPP_FTM_RANK OPP_FTA_RANK OPP_FT_PCT_RANK OPP_OREB_RANK OPP_DREB_RANK OPP_REB_RANK OPP_AST_RANK OPP_TOV_RANK OPP_STL_RANK OPP_BLK_RANK OPP_BLKA_RANK OPP_PF_RANK OPP_PFD_RANK OPP_PTS_RANK PLUS_MINUS_RANK
0 39.7 88.1 0.450 8.0 21.1 0.379 15.9 20.9 0.763 12.0 34.3 46.3 23.6 16.1 8.9 4.5 4.8 19.9 19.0 103.3 -0.4 16 21 13 19 14 28 4 3 26 17 24 25 13 11 28 6 23 8 28 10 13
1 38.2 86.8 0.441 7.2 21.0 0.342 18.7 25.3 0.740 12.1 34.0 46.1 22.9 16.2 8.3 4.9 4.8 20.6 22.4 102.4 0.2 5 8 7 8 13 4 24 24 8 20 22 24 10 9 14 10 22 11 7 7 16
2 41.2 89.0 0.464 7.0 19.0 0.366 15.4 21.1 0.731 11.8 31.4 43.2 23.6 14.8 8.3 5.2 5.3 23.6 20.2 104.9 -2.0 25 25 23 7 5 21 2 4 1 15 5 5 14 24 12 16 17 27 23 16 10
3 41.7 88.6 0.471 9.4 24.2 0.388 17.5 23.4 0.747 12.7 34.7 47.4 27.1 15.1 7.5 7.4 6.3 22.9 20.5 110.3 9.9 29 24 27 30 30 29 9 14 9 28 25 28 30 21 2 30 6 26 21 29 30
4 39.0 88.0 0.443 6.1 17.5 0.346 18.1 24.5 0.739 12.1 33.1 45.2 20.7 15.1 8.0 6.4 5.6 21.6 21.7 102.1 -0.3 10 19 9 2 1 5 19 22 7 19 14 17 1 22 8 25 13 18 13 6 14
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
25 41.5 86.5 0.480 13.1 33.7 0.387 18.4 23.0 0.800 9.1 33.3 42.4 26.8 13.8 7.4 4.5 4.2 18.3 19.8 114.5 -1.7 9 2 21 15 9 29 25 24 27 1 17 7 17 12 16 4 29 10 7 15 17
26 43.8 90.0 0.487 12.4 33.2 0.373 15.6 20.0 0.784 10.2 34.0 44.2 27.3 13.1 8.7 4.5 6.2 17.5 16.8 115.6 6.3 25 20 23 10 7 23 5 5 16 8 24 21 21 22 28 5 3 2 28 22 24
27 44.5 90.7 0.491 13.6 36.1 0.376 15.6 19.7 0.789 11.2 33.8 44.9 28.5 13.8 7.2 5.8 4.7 18.0 18.3 118.2 6.4 29 26 26 24 20 24 4 3 19 24 21 25 28 14 11 23 20 8 21 26 25
28 44.1 90.7 0.487 14.6 37.1 0.395 16.5 21.4 0.771 10.9 31.0 41.8 29.5 12.2 8.5 6.4 5.5 19.0 18.4 119.4 4.8 27 25 22 29 26 30 9 11 5 22 2 3 30 29 27 28 10 19 19 30 23
29 44.6 90.0 0.496 11.9 32.9 0.362 18.1 23.2 0.777 11.7 35.7 47.4 28.2 13.6 7.7 5.9 4.9 17.4 19.3 119.2 9.0 30 21 29 6 6 13 24 25 10 30 30 30 26 18 21 25 17 1 8 29 27

360 rows × 42 columns

Az nba_api általában megbízhatóan tölti le az adatokat, ezért nincs hiányzó adat ebben a táblában:

In [7]:
team_stats_all.isna().sum()
Out[7]:
Measure    Stat                   
TEAM_ID                               0
TEAM_NAME                             0
GP                                    0
W                                     0
L                                     0
                                     ..
Defense    OPP_PTS_OFF_TOV_RANK       0
           OPP_PTS_2ND_CHANCE_RANK    0
           OPP_PTS_FB_RANK            0
           OPP_PTS_PAINT_RANK         0
SEASON                                0
Length: 205, dtype: int64
In [8]:
# print column names that contain nan values
print(team_stats_all.columns[team_stats_all.isnull().any()].tolist())

[]

Statisztikai trendek vizsgálata¶

Base statisztikai csoport¶
In [9]:
# Assuming team_stats_simple is already loaded and contains the data
# Dictionary for statistic names
stat_name_dict = {
    "FGM": "Field Goals Made", "FGA": "Field Goals Attempted", "FG_PCT": "Field Goal Percentage",
    "FG3M": "Three-Point Field Goals Made", "FG3A": "Three-Point Field Goals Attempted", "FG3_PCT": "Three-Point Field Goal Percentage",
    "FTM": "Free Throws Made", "FTA": "Free Throws Attempted", "FT_PCT": "Free Throw Percentage",
    "OREB": "Offensive Rebounds", "DREB": "Defensive Rebounds", "REB": "Total Rebounds",
    "AST": "Assists", "TOV": "Turnovers", "STL": "Steals", "BLK": "Blocks", "BLKA": "Blocks Against",
    "PF": "Personal Fouls", "PFD": "Personal Fouls Drawn", "PTS": "Points"
}

# Plot boxplots for each statistic per season
for col in ["FGM", "FGA", "FG_PCT", "FG3M", "FG3A", "FG3_PCT", "FTM", "FTA", "FT_PCT", "OREB", "DREB", "REB", "AST", "TOV", "STL", "BLK", "BLKA", "PF", "PFD", "PTS"]:
    plt.figure(figsize=(10, 6))
    sns.boxplot(data=team_stats_simple, x="SEASON", y=col)
    plt.title(f"{stat_name_dict[col]} per 100 possessions Over 12 Seasons")
    plt.xlabel("Season")
    plt.ylabel(stat_name_dict[col])
    plt.xticks(rotation=45)
    plt.grid(True)
    plt.tight_layout()
    plt.show()
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A Base csoport trendjei között a legegyértelműbb, hogy egyre több pontot dobtak a csapatok az évek során. Ez leginkább annak köszönhető, hogy a ráemelt, és bedobott hárompontosok is hasonló trendet mutat. Érdekesség, hogy egyre kevesebb faultot harcoltak ki a csapatok, aminek köszönhetően egyre kevesebb büntetőt is dobtak rá. Ellenben ezeket egyre pontosabban értékesítették. Ezekell párhuzamosan több téren pontosabbak lettek a játékosok: az eladott labdák száma csökkenő trendet mutatott, a mezőnydobások hatékonysága pedig enyhén emelkedőt.

In [10]:
# Assuming team_stats_simple is already loaded and contains the data
# Dictionary for statistic names
stat_name_dict = {
    "OFF_RATING": "Offensive Rating", "DEF_RATING": "Defensive Rating", "NET_RATING": "Net Rating",
    "AST_TO": "Assist to Turnover Ratio", "AST_RATIO": "Assist Ratio", "OREB_PCT": "Offensive Rebound Percentage",
    "DREB_PCT": "Defensive Rebound Percentage", "REB_PCT": "Rebound Percentage", "TM_TOV_PCT": "Team Turnover Percentage",
    "EFG_PCT": "Effective Field Goal Percentage", "TS_PCT": "True Shooting Percentage", "PACE": "Pace",
    "PACE_PER40": "Pace Per 40 Minutes", "POSS": "Possessions", "PIE": "Player Impact Estimate"

}

# Plot boxplots for each statistic per season
for col in ["OFF_RATING","DEF_RATING","NET_RATING","AST_TO","OREB_PCT","DREB_PCT","TM_TOV_PCT","EFG_PCT","TS_PCT","PACE","POSS","PIE"]:
    plt.figure(figsize=(10, 6))
    sns.boxplot(data=team_stats_simple, x="SEASON", y=col)
    plt.title(f"{stat_name_dict[col]} per 100 possessions Over 12 Seasons")
    plt.xlabel("Season")
    plt.ylabel(stat_name_dict[col])
    plt.xticks(rotation=45)
    plt.grid(True)
    plt.tight_layout()
    plt.show()
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A legtöbb Advanced statisztika trendje jól magyarázható a korábban látottakkal. A dobott pontok növekvő trendje természetesen az Offensive ratinget (és Defensive ratinget is) magával húzta. Az, hogy egyre több triplát dobtak a csapatok, viszonylag hasonló százalékkal, mint korábban, egyre növekvő Effektív mezőnyszázalékokat eredményezett (az effektív mezőnyszázalék a dobható pontokkal súlyozott mezőnyszázalék). A támadó boom valószínűleg annak is köszönhető, hogy egyre gyorsabban játszanak a csapatok. Erre utal a növekvő Pace és Possessions (labdabirtoklás) trend.

In [11]:
# Assuming team_stats_simple is already loaded and contains the data
# Dictionary for statistic names
stat_name_dict = {
    "PTS_OFF_TOV": "Points Off Turnovers", "PTS_2ND_CHANCE": "Second Chance Points", "PTS_FB": "Fast Break Points",
    "PTS_PAINT": "Points in the Paint", "OPP_PTS_OFF_TOV": "Opponent Points Off Turnovers",
    "OPP_PTS_2ND_CHANCE": "Opponent Second Chance Points", "OPP_PTS_FB": "Opponent Fast Break Points",
    "OPP_PTS_PAINT": "Opponent Points in the Paint"
}

# Plot boxplots for each statistic per season
for col in ["PTS_OFF_TOV","PTS_2ND_CHANCE","PTS_FB","PTS_PAINT","OPP_PTS_OFF_TOV","OPP_PTS_2ND_CHANCE","OPP_PTS_FB","OPP_PTS_PAINT"]:
    plt.figure(figsize=(10, 6))
    sns.boxplot(data=team_stats_simple, x="SEASON", y=col)
    plt.title(f"{stat_name_dict[col]} per 100 possessions Over 12 Seasons")
    plt.xlabel("Season")
    plt.ylabel(stat_name_dict[col])
    plt.xticks(rotation=45)
    plt.grid(True)
    plt.tight_layout()
    plt.show()
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A Misc csoportban az egyetlen nem konstans trend a festékből dobott pontoknál figyelhető meg. Ez (természetesen) mind a csapatoknál, mind ellenfeleiknél pozitív.

In [12]:
# Assuming team_stats_simple is already loaded and contains the data
# Dictionary for statistic names
stat_name_dict = {
    "PCT_FGA_2PT": "Percentage of Field Goals Attempted that are 2-Pointers",
    "PCT_FGA_3PT": "Percentage of Field Goals Attempted that are 3-Pointers",
    "PCT_PTS_2PT": "Percentage of Points that are 2-Pointers",
    "PCT_PTS_2PT_MR": "Percentage of Points that are Mid-Range 2-Pointers",
    "PCT_PTS_3PT": "Percentage of Points that are 3-Pointers",
    "PCT_PTS_FB": "Percentage of Points that are Fast Break Points",
    "PCT_PTS_FT": "Percentage of Points that are Free Throws",
    "PCT_PTS_OFF_TOV": "Percentage of Points that came after Turnovers",
    "PCT_PTS_PAINT": "Percentage of Points that are in the Paint",
    "PCT_AST_2PM": "Percentage of 2-Point Field Goals that are Assisted",
    "PCT_UAST_2PM": "Percentage of 2-Point Field Goals that are Unassisted",
    "PCT_AST_3PM": "Percentage of 3-Point Field Goals that are Assisted",
    "PCT_UAST_3PM": "Percentage of 3-Point Field Goals that are Unassisted",
    "PCT_AST_FGM": "Percentage of Field Goals Made that are Assisted",
    "PCT_UAST_FGM": "Percentage of Field Goals Made that are Unassisted"
}

# Plot boxplots for each statistic per season
for col in ["PCT_FGA_2PT","PCT_FGA_3PT","PCT_PTS_2PT","PCT_PTS_2PT_MR","PCT_PTS_3PT","PCT_PTS_FB","PCT_PTS_FT","PCT_PTS_OFF_TOV","PCT_PTS_PAINT","PCT_AST_2PM","PCT_UAST_2PM","PCT_AST_3PM","PCT_UAST_3PM","PCT_AST_FGM","PCT_UAST_FGM"]:
    plt.figure(figsize=(10, 6))
    sns.boxplot(data=team_stats_simple, x="SEASON", y=col)
    plt.title(f"{stat_name_dict[col]} per 100 possessions Over 12 Seasons")
    plt.xlabel("Season")
    plt.ylabel(stat_name_dict[col])
    plt.xticks(rotation=45)
    plt.grid(True)
    plt.tight_layout()
    plt.show()
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A Scoring csoport trendjei szintén várhatónak mondhatóak az előzőek alapján. A csapatok eltolták a dobás kísérleteket a hárompontosak irányába, aminek a legnagyobb elszenvedője a középtávoli (Mid Range) dobás volt, ami tulajdonképpen eltűnt a ligából (legalábbis az alapszakaszban, a rájátszás során még mindig nagyon fontos jól dobni ezt a dobásfajtát). A pontok egyre kevesebb százaléka érkezett büntetőkből, valamint eladott labdák után, ami szintén nem meglepő az előzőek fényében.

A többi csoport statisztikái vagy szerepeltek már, vagy nem lenne értelme a trendeket vizsgálni, ezért ezeket mellőzöm.

Liga szintű dobás statisztikák zónánként¶

In [13]:
shooting_averages = pd.read_csv("data/shooting_averages_2012_to_2023.csv")
shooting_averages
Out[13]:
GRID_TYPE SHOT_ZONE_BASIC SHOT_ZONE_AREA SHOT_ZONE_RANGE FGA FGM FG_PCT SEASON
0 League Averages Above the Break 3 Back Court(BC) Back Court Shot 72 2 0.028 2012-13
1 League Averages Above the Break 3 Center(C) 24+ ft. 8133 2795 0.344 2012-13
2 League Averages Above the Break 3 Left Side Center(LC) 24+ ft. 14414 5001 0.347 2012-13
3 League Averages Above the Break 3 Right Side Center(RC) 24+ ft. 14958 5352 0.358 2012-13
4 League Averages Backcourt Back Court(BC) Back Court Shot 455 8 0.018 2012-13
... ... ... ... ... ... ... ... ...
235 League Averages Mid-Range Right Side Center(RC) 16-24 ft. 3736 1479 0.396 2023-24
236 League Averages Mid-Range Right Side(R) 16-24 ft. 1309 530 0.405 2023-24
237 League Averages Mid-Range Right Side(R) 8-16 ft. 5450 2355 0.432 2023-24
238 League Averages Restricted Area Center(C) Less Than 8 ft. 68611 45421 0.662 2023-24
239 League Averages Right Corner 3 Right Side(R) 24+ ft. 11335 4445 0.392 2023-24

240 rows × 8 columns

In [50]:
shooting_averages.describe()
Out[50]:
FGA FGM FG_PCT
count 240.000000 240.000000 240.000000
mean 11087.104167 5098.925000 0.372750
std 15056.302633 9216.042705 0.122887
min 34.000000 2.000000 0.012000
25% 2237.000000 973.500000 0.365500
50% 6986.000000 2802.500000 0.394500
75% 12269.750000 5006.250000 0.417000
max 76666.000000 48198.000000 0.665000
In [51]:
shooting_averages.isna().sum()
Out[51]:
GRID_TYPE          0
SHOT_ZONE_BASIC    0
SHOT_ZONE_AREA     0
SHOT_ZONE_RANGE    0
FGA                0
FGM                0
FG_PCT             0
SEASON             0
dtype: int64

Ahogy korábban említettem, két féle dobás zóna csoportosítást használ az NBA. Először az egyszerűbbet mutatom be:

In [14]:
shooting_averages.SHOT_ZONE_BASIC.unique()
Out[14]:
array(['Above the Break 3', 'Backcourt', 'In The Paint (Non-RA)',
       'Left Corner 3', 'Mid-Range', 'Restricted Area', 'Right Corner 3'],
      dtype=object)
In [21]:
from plotting_functions import draw_paint

fig,ax = plt.subplots(figsize=(12,12))
draw_court(ax,outer_lines=True)
draw_paint(ax)

xlim=(-250, 250)
ylim=(422.5, -47.5)
ax.set_xlim(xlim)
ax.set_ylim(ylim)

ax.tick_params(labelbottom="off", labelleft="off")
ax.set_title("Paint zone", fontsize=18)
plt.show()
No description has been provided for this image
In [22]:
from plotting_functions import draw_restricted_area

fig,ax = plt.subplots(figsize=(12,12))
draw_court(ax,outer_lines=True)
draw_restricted_area(ax)

xlim=(-250, 250)
ylim=(422.5, -47.5)
ax.set_xlim(xlim)
ax.set_ylim(ylim)

ax.tick_params(labelbottom="off", labelleft="off")
ax.set_title("Restricted Area", fontsize=18)
plt.show()
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In [26]:
from plotting_functions import draw_midrange

fig,ax = plt.subplots(figsize=(12,12))
draw_court(ax,outer_lines=True)
draw_midrange(ax)

xlim=(-250, 250)
ylim=(422.5, -47.5)
ax.set_xlim(xlim)
ax.set_ylim(ylim)

ax.tick_params(labelbottom="off", labelleft="off")
ax.set_title("Midrange zone", fontsize=18)
plt.show()
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In [20]:
from plotting_functions import draw_court, draw_above_the_break_3

fig,ax = plt.subplots(figsize=(12,12))
draw_court(ax,outer_lines=True)
draw_above_the_break_3(ax)

xlim=(-250, 250)
ylim=(422.5, -47.5)
ax.set_xlim(xlim)
ax.set_ylim(ylim)

ax.tick_params(labelbottom="off", labelleft="off")
ax.set_title("Above the Break 3 zone", fontsize=18)
plt.show()
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In [23]:
from plotting_functions import draw_left_three_point_corner

fig,ax = plt.subplots(figsize=(12,12))
draw_court(ax,outer_lines=True)
draw_left_three_point_corner(ax)

xlim=(-250, 250)
ylim=(422.5, -47.5)
ax.set_xlim(xlim)
ax.set_ylim(ylim)

ax.tick_params(labelbottom="off", labelleft="off")
ax.set_title("Left Corner 3 zone", fontsize=18)
plt.show()
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In [25]:
from plotting_functions import draw_right_three_point_corner

fig,ax = plt.subplots(figsize=(12,12))
draw_court(ax,outer_lines=True)
draw_right_three_point_corner(ax)

xlim=(-250, 250)
ylim=(422.5, -47.5)
ax.set_xlim(xlim)
ax.set_ylim(ylim)

ax.tick_params(labelbottom="off", labelleft="off")
ax.set_title("Right Corner 3 zone", fontsize=18)
plt.show()
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A Backcourt zóna a játékos saját térfele, ezt ne mutatom meg.

A másik csoportosítást ritkábban használják, ezeket konkrét dobásokkal mutatom meg.

In [27]:
shooting_averages.SHOT_ZONE_AREA.unique()
Out[27]:
array(['Back Court(BC)', 'Center(C)', 'Left Side Center(LC)',
       'Right Side Center(RC)', 'Left Side(L)', 'Right Side(R)'],
      dtype=object)
In [29]:
shotchart_2012 = pd.read_csv('data/nba_shotdata_2012.csv')
In [30]:
from plotting_functions import shot_chart

fig, ax = plt.subplots(figsize=(12,10))
shot_chart(data=shotchart_2012[shotchart_2012["SHOT_ZONE_AREA"] == "Center(C)"], x='LOC_X', y='LOC_Y', kind='scatter', title='Center(C) zone', cmap="Reds", ax=ax, draw_zones=False)
plt.show()
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In [31]:
from plotting_functions import shot_chart

fig, ax = plt.subplots(figsize=(12,10))
shot_chart(data=shotchart_2012[shotchart_2012["SHOT_ZONE_AREA"] == 'Left Side Center(LC)'], x='LOC_X', y='LOC_Y', kind='scatter', title='Left Side Center(LC) zone', cmap="Reds", ax=ax, draw_zones=False)
plt.show()
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In [32]:
from plotting_functions import shot_chart

fig, ax = plt.subplots(figsize=(12,10))
shot_chart(data=shotchart_2012[shotchart_2012["SHOT_ZONE_AREA"] == 'Right Side Center(RC)'], x='LOC_X', y='LOC_Y', kind='scatter', title='Right Side Center(RC) zone', cmap="Reds", ax=ax, draw_zones=False)
plt.show()
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In [33]:
from plotting_functions import shot_chart

fig, ax = plt.subplots(figsize=(12,10))
shot_chart(data=shotchart_2012[shotchart_2012["SHOT_ZONE_AREA"] == 'Left Side(L)'], x='LOC_X', y='LOC_Y', kind='scatter', title='Left Side(L) zone', cmap="Reds", ax=ax, draw_zones=False)
plt.show()
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In [34]:
from plotting_functions import shot_chart

fig, ax = plt.subplots(figsize=(12,10))
shot_chart(data=shotchart_2012[shotchart_2012["SHOT_ZONE_AREA"] == 'Right Side(R)'], x='LOC_X', y='LOC_Y', kind='scatter', title='Right Side(R) zone', cmap="Reds", ax=ax, draw_zones=False)
plt.show()
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In [43]:
shooting_averages
Out[43]:
GRID_TYPE SHOT_ZONE_BASIC SHOT_ZONE_AREA SHOT_ZONE_RANGE FGA FGM FG_PCT SEASON
0 League Averages Above the Break 3 Back Court(BC) Back Court Shot 72 2 0.028 2012-13
1 League Averages Above the Break 3 Center(C) 24+ ft. 8133 2795 0.344 2012-13
2 League Averages Above the Break 3 Left Side Center(LC) 24+ ft. 14414 5001 0.347 2012-13
3 League Averages Above the Break 3 Right Side Center(RC) 24+ ft. 14958 5352 0.358 2012-13
4 League Averages Backcourt Back Court(BC) Back Court Shot 455 8 0.018 2012-13
... ... ... ... ... ... ... ... ...
235 League Averages Mid-Range Right Side Center(RC) 16-24 ft. 3736 1479 0.396 2023-24
236 League Averages Mid-Range Right Side(R) 16-24 ft. 1309 530 0.405 2023-24
237 League Averages Mid-Range Right Side(R) 8-16 ft. 5450 2355 0.432 2023-24
238 League Averages Restricted Area Center(C) Less Than 8 ft. 68611 45421 0.662 2023-24
239 League Averages Right Corner 3 Right Side(R) 24+ ft. 11335 4445 0.392 2023-24

240 rows × 8 columns

In [42]:
shots_attempted = shooting_averages.groupby(["SEASON", "SHOT_ZONE_BASIC"]).sum().reset_index()
shots_attempted
Out[42]:
SEASON SHOT_ZONE_BASIC GRID_TYPE SHOT_ZONE_AREA SHOT_ZONE_RANGE FGA FGM FG_PCT
0 2012-13 Above the Break 3 League AveragesLeague AveragesLeague AveragesL... Back Court(BC)Center(C)Left Side Center(LC)Rig... Back Court Shot24+ ft.24+ ft.24+ ft. 37577 13150 1.077
1 2012-13 Backcourt League Averages Back Court(BC) Back Court Shot 455 8 0.018
2 2012-13 In The Paint (Non-RA) League AveragesLeague AveragesLeague AveragesL... Center(C)Center(C)Left Side(L)Right Side(R) 8-16 ft.Less Than 8 ft.8-16 ft.8-16 ft. 31355 12115 1.582
3 2012-13 Left Corner 3 League Averages Left Side(L) 24+ ft. 7534 2906 0.386
4 2012-13 Mid-Range League AveragesLeague AveragesLeague AveragesL... Center(C)Center(C)Left Side Center(LC)Left Sid... 16-24 ft.8-16 ft.16-24 ft.8-16 ft.16-24 ft.16-... 60849 23832 3.154
... ... ... ... ... ... ... ... ...
79 2023-24 In The Paint (Non-RA) League AveragesLeague AveragesLeague AveragesL... Center(C)Center(C)Left Side(L)Right Side(R) 8-16 ft.Less Than 8 ft.8-16 ft.8-16 ft. 45779 20150 1.767
80 2023-24 Left Corner 3 League Averages Left Side(L) 24+ ft. 12242 4701 0.384
81 2023-24 Mid-Range League AveragesLeague AveragesLeague AveragesL... Center(C)Center(C)Left Side Center(LC)Left Sid... 16-24 ft.8-16 ft.16-24 ft.8-16 ft.16-24 ft.16-... 26321 11065 3.358
82 2023-24 Restricted Area League Averages Center(C) Less Than 8 ft. 68611 45421 0.662
83 2023-24 Right Corner 3 League Averages Right Side(R) 24+ ft. 11335 4445 0.392

84 rows × 8 columns

In [47]:
# Plot the data
plt.figure(figsize=(14, 8))
sns.lineplot(data=shooting_averages, x="SEASON", y="FGA", hue="SHOT_ZONE_BASIC", marker="o", errorbar=None)
plt.title("Number of Shots Attempted per SHOT_ZONE_BASIC Over the Seasons")
plt.xlabel("Season")
plt.ylabel("Number of Shots Attempted")
plt.grid(True)
plt.tight_layout()
plt.show()
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Ahogy korábban is láttuk, itt is megerősödik, hogy az évek során egyre kevesebb középtávoli (Mid-Range) dobás kísérlet volt, és egyre több tripla. Érdekes viszont, hogy a 2018-19-es szeon után két évig egyre kevesebb dobást kíséreltek meg a csapatok a gyűrű közvetlen közeléből (Restricted Area).

In [36]:
shooting_averages.SHOT_ZONE_BASIC.value_counts()
Out[36]:
SHOT_ZONE_BASIC
Mid-Range                96
Above the Break 3        48
In The Paint (Non-RA)    48
Backcourt                12
Left Corner 3            12
Restricted Area          12
Right Corner 3           12
Name: count, dtype: int64

A zónák bemutatása során látottakhoz a dobásokat tartalmazó adathalmaz bemutatása sokban nem tenne hozzá, ezért betöltés után csak kiíratom az alapvető statisztikákat és a nanok számát.

In [48]:
shots_all = pd.read_csv("data/nba_shots_2012_to_2023.csv")
shots_all.describe()
Out[48]:
GAME_ID GAME_EVENT_ID PLAYER_ID TEAM_ID PERIOD MINUTES_REMAINING SECONDS_REMAINING SHOT_DISTANCE LOC_X LOC_Y SHOT_ATTEMPTED_FLAG SHOT_MADE_FLAG GAME_DATE
count 2.309464e+06 2.309464e+06 2.309464e+06 2.309464e+06 2.309464e+06 2.309464e+06 2.309464e+06 2.309464e+06 2.309464e+06 2.309464e+06 2309464.0 2.309464e+06 2.309464e+06
mean 2.175219e+07 2.940258e+02 6.747896e+05 1.610613e+09 2.478944e+00 5.352131e+00 2.881328e+01 1.295919e+01 -1.087900e+00 8.934664e+01 1.0 4.624900e-01 2.018205e+07
std 3.471963e+05 1.840646e+02 6.962373e+05 8.659441e+00 1.135482e+00 3.451284e+00 1.743528e+01 1.031636e+01 1.103814e+02 9.253451e+01 0.0 4.985911e-01 3.503711e+04
min 2.120000e+07 1.000000e+00 2.550000e+02 1.610613e+09 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 -2.500000e+02 -5.200000e+01 1.0 0.000000e+00 2.012103e+07
25% 2.150004e+07 1.340000e+02 2.019330e+05 1.610613e+09 1.000000e+00 2.000000e+00 1.400000e+01 2.000000e+00 -4.900000e+01 1.100000e+01 1.0 0.000000e+00 2.015103e+07
50% 2.170118e+07 2.880000e+02 2.031140e+05 1.610613e+09 2.000000e+00 5.000000e+00 2.900000e+01 1.300000e+01 0.000000e+00 4.800000e+01 1.0 0.000000e+00 2.018040e+07
75% 2.210014e+07 4.380000e+02 1.627788e+06 1.610613e+09 3.000000e+00 8.000000e+00 4.400000e+01 2.400000e+01 4.700000e+01 1.690000e+02 1.0 1.000000e+00 2.021111e+07
max 2.230123e+07 1.012000e+03 1.642013e+06 1.610613e+09 8.000000e+00 1.200000e+01 5.900000e+01 8.800000e+01 2.500000e+02 8.780000e+02 1.0 1.000000e+00 2.024041e+07
In [49]:
shots_all.isna().sum()
Out[49]:
GRID_TYPE              0
GAME_ID                0
GAME_EVENT_ID          0
PLAYER_ID              0
PLAYER_NAME            0
TEAM_ID                0
TEAM_NAME              0
PERIOD                 0
MINUTES_REMAINING      0
SECONDS_REMAINING      0
EVENT_TYPE             0
ACTION_TYPE            0
SHOT_TYPE              0
SHOT_ZONE_BASIC        0
SHOT_ZONE_AREA         0
SHOT_ZONE_RANGE        0
SHOT_DISTANCE          0
LOC_X                  0
LOC_Y                  0
SHOT_ATTEMPTED_FLAG    0
SHOT_MADE_FLAG         0
GAME_DATE              0
HTM                    0
VTM                    0
dtype: int64

Megválaszolandó kérdések¶

  • Mi volt a kívánatos dobási térkép az egyes években?
  • Milyen statisztikai mutatókban kellet jól teljesíteni ahhoz, hogy jó támadást/védekezést rakjon össze egy csapat?

Elemzés és vizualizáció jellege, technólógiája¶

A feladat végén egy dash+plotly weboldalt szeretnék készíteni, ahol a csapatok dobástérképét és statisztikai mutatóit lehet megnézni, egy dashboardhoz hasonlóan.